Preservice Teacher Knowledge, Print Exposure, and Planning for Instruction
Bibliographic record
Abstract
Abstract Teachers who are knowledgeable about the basic structure of the English language incorporate this knowledge into their instruction. In this study, the authors explored a similar relation between knowledge of print exposure and planning for a grade 5 classroom. The personal reading experience (print exposure) of 106 preservice teachers was measured for three genres: storybooks, children's and young adult literature, and adult fiction. Teacher knowledge was measured by two tasks: defining terms and evaluating instructional practices. Planning for instruction was measured by asking participants to plan for a week of grade 5 language arts instruction. Correlational analyses revealed that print exposure, teacher knowledge, and time allocated for student reading in a grade 5 classroom were positively related. Furthermore, regression analyses revealed that familiarity with authors of children's and young adult literature accounted for significant variance on both knowledge tasks even after controlling for other forms of print exposure (storybooks and adult fiction). The data suggest that knowledge about print exposure and personal reading experience, especially of children's and young adult literature, are both associated with planning for instruction in the upper elementary grades. The results are discussed in relation to teacher training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".